1. Technical Field
The present invention relates generally to pattern recognition and classification and more specifically to systems and methods for arbitrating the results of multiple recognizers
2. Description of the Prior Art
Pattern recognition systems, loosely defined, are systems capable of distinguishing between various classes of real world stimuli according to their divergent characteristics. A number of applications require pattern recognition systems, which allow a system to deal with unrefined data without significant human intervention. By way of example, a pattern recognition system may attempt to recognize individual letters to reduce a handwritten document to electronic text. Alternatively, the system may recognize spoken utterances to allow verbal commands to be received at a computer console. In order to classify real-world stimuli, however, it is necessary to train a recognizer to discriminate between classes by exposing it to a number of sample patterns.
In many applications, it is difficult or impossible to train a recognizer on all of the possible classes of patterns that can occur during operation. For example, in a face recognition application operating in a public location, it is impossible to train a recognizer to recognize all of the millions of individuals who could conceivably be encountered by the recognizer. With a very real possibility that the recognizer can encounter stimuli from an unknown class in a given situation, it becomes important to distinguish accurate recognizer results from false positives, in which the recognizer assigns an existing class to a stimuli belonging to an unknown class. In many applications, these false positives can cause a large degree of inconvenience and expense.